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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A Snowpark application is configured to connect to Snowflake using environment variables for authentication. However, the application frequently encounters intermittent connection errors. You suspect that the environment variables are not being correctly accessed by the Snowpark session builder. Which of the following actions could help diagnose and resolve the issue? (Select TWO)

A) Verify that the user account specified in the environment variables has the necessary permissions (e.g., USAGE on the warehouse, database, and schema) in Snowflake.
B) Use to explicitly set connection parameters instead of relying on environment variables for debugging.
C) Check that the environment variables are set at the correct scope (e.g., system-level, user-level, or within the application's execution environment) and are accessible to the Python process running the Snowpark application.
D) Explicitly print the values of the relevant environment variables (e.g., 'SNOWFLAKE_USER , 'SNOWFLAKE_PASSWORD, 'SNOWFLAKE_ACCOUNT) within the Snowpark application before attempting to create the session.
E) Replace 'session.builder.getorcreate()' with 'Session.builder.appName('MySnowparkApp').create()'

2. Consider the following Snowpark Python code snippet designed to perform a join operation between two large tables: 'transactions' and 'customers'. The 'transactions' table contains billions of rows and the 'customers' table contains millions of rows. You are experiencing performance bottlenecks during the join operation. The 'transactions' table has a 'customer id' column that references the 'customers' table's primary key 'id'. Which optimization techniques would be MOST effective in improving the join performance within a Snowpark- optimized warehouse?

A) Broadcast the smaller DataFrame (customers') to all nodes in the warehouse before performing the join. Use: 'from snowflake.snowpark.functions import broadcast; df_transactions.join(broadcast(df_customers), df_transactions['customer_id']
B) Increase the size of the Snowpark-optimized warehouse to provide more memory and CPU resources for the join operation.
C) Explicitly specify the join type as a broadcast join using a hint: 'df_transactions.join(df_customers, df_transactions['customer_id'] == df_customers['id'],
D) Repartition the 'transactions DataFrame by the 'customer_id' column and the 'customers' DataFrame by the 'id' column before performing the join using 'df_transactions.repartition('customer_id').join(df_customers.repartition('id'), df_transactions['customer_id'] == df_customers['id'])'.
E) Ensure that the 'customer_id' column in the 'transactions' table and the 'id' column in the 'customers' table have appropriate indexes defined in Snowflake before performing the join.

3. You have a Snowflake table 'orders_json' with a VARIANT column named "order details'. This column contains JSON objects, and one of the fields within these objects is an array called 'items'. You need to use Snowpark to flatten the 'items' array into rows, extracting the 'item_id' , 'item_name' , and 'quantity' from each item in the array. Which of the following Snowpark code snippets will correctly achieve this, assuming 'df is a DataFrame representing 'orders_json'?

A)

B)

C)

D)

E)

4. Consider the following Snowpark Python code snippet that retrieves data and calculates aggregate values, however, the application performance is slow when you are fetching dataframe, given the 'block' parameter controls the synchronous/asynchronous behavior of the 'collect()' method. Choose ALL the statements about "session.create_dataframe([rowl ,row2],schema)' that are correct:

A) Consider replacing the call of 'collect()' with and converting Snowpark dataframe to Pandas dataframe. The dataframe operations could be efficient.
B) Increasing warehouse size of Snowflake will increase performance irrespective of the fact whether block is True or False.
C) Snowflake warehouse size has no influence on dataframe creation, so if the dataframe is large and 'block=True' it will block the performance.
D) The 'block' parameter has no influence on performance when creating dataframes, and hence the performance is mainly based on query optimization and ware house size.
E) The method, when executed with default 'block=True' , will wait for the result to be available before proceeding to the other statements in the code and hence performance is low. However, makes it asynchronous.

5. You are developing a Snowpark application to analyze customer data'. You need to create a Snowpark DataFrame from a list of dictionaries, where each dictionary represents a customer with 'id', 'name', and 'city' keys. The data should be loaded efficiently. Consider these scenarios: 1 . The input data can sometimes contain missing values (e.g., a customer might not have a city specified). 2. You want to ensure optimal performance when loading the data, as the list can be very large. 3. You need the resulting DataFrame's schema to correctly infer the datatypes based on the input dictionary's values. Which of the following methods and considerations should be used to create a Snowpark DataFrame from a list of dictionaries to meet these requirements?

A) Use 'session.createDataFrame(data, and specify the 'nullable' property for each field within the schema. Additionally, define the data type explicitly. This optimizes performance.
B) Use 'session.createDataFrame(data, where 'schema' is explicitly defined to handle missing data and ensure correct data types. This improves performance over schema inference.
C) Use 'session.createDataFrame(data)' with default settings. Snowflake will automatically infer the schema and handle missing values as 'NULL'.
D) Use 'session.createDataFrame(data)' and then explicitly cast columns with potential missing values to the correct datatype using method to ensure 'NULL' handling.
E) Leverage Snowpark's optimized data loading by converting the list of dictionaries to a Pandas DataFrame first and then create a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'. Pandas has optimized data loading.

Solutions: